Zone storage data placement

ABSTRACT

A method includes: receiving, by a computing device, a data slice for storage in a dispersed storage network; predicting, by the computing device, a modification frequency associated with the data slice; and storing, by the computing device, the data slice in one of a first type zone of a data storage and a second type zone of the data storage based on the predicted modification frequency.

BACKGROUND

Aspects of the present invention relate generally to managing data indispersed storage networks and, more particularly, to systems andmethods for storing and moving data in particular zones in a dispersedstorage network.

Computing devices communicate data, process data, and/or store data.Such computing devices range from wireless smart phones, laptops,tablets, personal computers (PC), work stations, and video game devices,to data centers that support millions of web searches, stock trades, oron-line purchases every day. In general, a computing device includes acentral processing unit (CPU), a memory system, user input/outputinterfaces, peripheral device interfaces, and an interconnecting busstructure.

A computer may effectively extend its CPU by using “cloud computing” toperform one or more computing functions (e.g., a service, anapplication, an algorithm, an arithmetic logic function, etc.) on behalfof the computer. Further, for large services, applications, and/orfunctions, cloud computing may be performed by multiple cloud computingresources in a distributed manner to improve the response time forcompletion of the service, application, and/or function. For example,Hadoop® is an open source software framework that supports distributedapplications enabling application execution by thousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. Cloud storage enables a user, via itscomputer, to store files, applications, etc., on an Internet storagesystem. The Internet storage system may include a RAID (redundant arrayof independent disks) system and/or a dispersed storage system that usesan error correction scheme to encode data for storage.

SUMMARY

In a first aspect of the invention, there is a computer-implementedmethod including: receiving, by a computing device, a data slice forstorage in a dispersed storage network; predicting, by the computingdevice, a modification frequency associated with the data slice; andstoring, by the computing device, the data slice in one of a first typezone of a data storage and a second type zone of the data storage basedon the predicted modification frequency.

In another aspect of the invention, there is a computer program product,the computer program product comprising one or more computer readablestorage media having program instructions collectively stored on the oneor more computer readable storage media, the program instructionsexecutable to: define different zones of a data storage of a dispersedstorage unit in a dispersed storage network, the different zonesincluding first type zones, second type zones, and third type zones;receive plural data slices for storage in the dispersed storage unit;store a first subset of the plural data slices in a said first type zonebased on predicting modification frequencies associated with slices inthe first subset; store a second subset of the plural data slices, thatis mutually exclusive of the first subset of the plural data slices, ina said second type zone; reclaim the said first type zone withoutperforming any I/O operations; and reclaim the said second type zone bywriting live slices contained in the said second type zone to a thirdtype zone and then reallocating the said second type zone.

In another aspect of the invention, there is system including adispersed storage unit in a dispersed storage network, the dispersedstorage unit comprising a processor, a computer readable memory, one ormore computer readable storage media, and program instructionscollectively stored on the one or more computer readable storage mediafor execution by the processor. Execution of the program instructionscauses the dispersed storage unit to: define different zones of a datastorage of the dispersed storage unit, the different zones includingfirst type zones, second type zones, and third type zones; receiveplural data slices for storage in the dispersed storage unit; store afirst subset of the plural data slices in a said first type zone basedon predicting modification frequencies associated with slices in thefirst subset; store a second subset of the plural data slices, that ismutually exclusive of the first subset of the plural data slices in asaid second type zone; reclaim the said first type zone withoutperforming any I/O operations; and reclaim the said second type zone bywriting live slices contained in the said second type zone to a thirdtype zone and then reallocating the said second type zone.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present invention are described in the detaileddescription which follows, in reference to the noted plurality ofdrawings by way of non-limiting examples of exemplary embodiments of thepresent invention.

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4 shows a dispersed storage network (DSN) in accordance withaspects of the invention.

FIG. 5 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with aspects of the presentinvention.

FIG. 6 is a schematic block diagram of a generic example of an errorencoding function in accordance with aspects of the present invention.

FIG. 7 is a schematic block diagram of a specific example of an errorencoding function in accordance with aspects of the present invention.

FIG. 8 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with aspects of the presentinvention.

FIG. 9 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with aspects of the presentinvention.

FIG. 10 is a schematic block diagram of a generic example of an errordecoding function in accordance with aspects of the present invention.

FIG. 11 shows a flowchart of an exemplary method in accordance withaspects of the invention.

DETAILED DESCRIPTION

Aspects of the present invention relate generally to managing data indispersed storage networks and, more particularly, to systems andmethods for storing and moving data in particular zones in a dispersedstorage network. In accordance with aspects of the invention, there is amethod for data placement based on data modification frequency in a zonestorage environment, comprising the steps of: creating a first storagezone containing data that is highly transient, a third zone containingdata that is highly tenured, and a second zone that contains data thatis a combination of transient and tenured; and placing data within thefirst zone, second zone, and third zone based on a prediction of datamodification frequency.

Aspects of the invention improve the functioning of a computer systemand technology by increasing the efficiency of the system. Inparticular, aspects of the invention improve the efficiency of adispersed storage network (DSN) generally, and improve the efficiency ofa dispersed storage units (DS units) specifically, by reducing thenumber of times each DS unit performs compaction processes to reclaimunused space in zones in a Zone Slice Storage (ZSS) based memory system.For example, by grouping together tenured data (e.g., slices) in aparticular type of zone in the ZSS, the system reduces the likelihoodthat that particular type of zone will reach the threshold that triggersreclamation via compaction. Since such reclamation has a cost in termsof DSN resources (i.e., I/O operations of the DS unit), reducing thefrequency of such reclamation, in the manner described herein, has aconcrete and tangible impact on the efficiency of the DS unitspecifically and the DSN as a whole system. This improvement in thefunctioning of a computer system and technology is a practicalapplication. Aspects of the invention are also rooted in computertechnology including dispersed storage networks (DSNs), Zone SliceStorage (ZSS), and Information Dispersal Algorithms (IDAs).

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium or media, as used herein, is not to beconstrued as being transitory signals per se, such as radio waves orother freely propagating electromagnetic waves, electromagnetic wavespropagating through a waveguide or other transmission media (e.g., lightpulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and zone storage 96.

Implementations of the invention may include a computer system/server 12of FIG. 1 in which one or more of the program modules 42 are configuredto perform (or cause the computer system/server 12 to perform) one ofmore functions of the zone storage 96 of FIG. 3. For example, the one ormore of the program modules 42 may be configured to: receive, by acomputing device, a data slice for storage in a dispersed storagenetwork; predict, by the computing device, a modification frequencyassociated with the data slice; and store, by the computing device, thedata slice in one of a first type zone of a data storage and a secondtype zone of the data storage based on the predicted modificationfrequency.

FIG. 4 shows a dispersed storage network 400 (referred to as DSN ordsNet) in accordance with aspects of the invention. In embodiments, theDSN 400 comprises plural dispersed storage processing units 410 (DSprocessing units), plural dispersed storage units 420 (DS units), and atleast one dispersed storage manager 430 (DS manager). The DS processingunits 410, the DS units 420, and the DS manager 430 all communicate viaa network 440, which comprises one or more computer networks such as aLAN, WAN, and the Internet. In a cloud implementation, the network 440is a cloud computing environment 50 of FIG. 2, and each of the DSprocessing units 410, the DS units 420, and the DS manager 430 are nodes10 in the cloud computing environment 50.

In accordance with aspects of the invention, the DSN 400 stores datausing object storage technology, which uses Information DispersalAlgorithms (IDAs) to separate a data object into slices that aredistributed to plural ones of the DS units 420. As used herein, a sliceis a dispersed piece of encoded data. Slices are created from anoriginal data object and can be used to recreate the original dataobject. In particular, the DSN 400 creates slices using a combination oferasure coding, encryption, and dispersal algorithms. The erasure codinggenerates ‘extra’ slices for each data object, such that the data objectcan be recreated from a subset (less than all of) the total number ofslices that are stored for this data object. By dividing a data objectinto slices and storing the slices at plural different DS units 420, theDSN 400 ensures that no single one of the DS units 420 has all theslices that are necessary to recreate the data object. Moreover, bycreating extra slices for each data object, the DSN 400 can toleratemultiple failures without losing the ability to recreate the originaldata object, e.g., from the available slices.

According to aspects of the invention, the DS manager 430 provides amanagement interface that is used for system administrative tasks, suchas system configuration, storage provisioning, and monitoring the healthand performance of the system. The DS manager 430 may comprise aphysical device (e.g., a computer device such as computer system/server12 of FIG. 1), a virtual machine (VM), or a container (e.g., a Dockercontainer). The term “Docker” may be subject to trademark rights invarious jurisdictions throughout the world and is used here only inreference to the products or services properly denominated by the markto the extent that such trademark rights may exist.

According to aspects of the invention, the DS processing units 410 areconfigured to encrypt and encode data during a write operation, tomanage the dispersal of slices of data during a write operation, and todecode and decrypt data during a read operation. In one example, duringa write operation, one or more of the DS processing units 410 areconfigured to generate data slices for storage by performing a dispersedstorage error encoding function on a set of data segments for storage,where the encoded data slices of a data segment are transmitted to aninformation dispersal algorithm (IDA) width threshold number of DS units420. In this example, during a read operation, one or more of the DSprocessing units 410 are configured to recover a data segment byretrieving at least an IDA decode threshold number of encoded dataslices from at least a corresponding IDA decode threshold number of DSunits 420, and by performing a dispersed storage error decoding functionon the received encoded data slices.

In embodiments, the DS processing units 410 are stateless componentsthat present a storage interface to a client application and thattransform data objects into slices using an IDA. Each DS processing unit410 may comprise a physical device (e.g., a computer device such as acomputer system/server 12 of FIG. 1), a virtual machine (VM), or acontainer (e.g., a Docker container).

In embodiments, each DS processing unit 410 comprises a DS processingunit program module 415 that is configured to perform processes of theDS processing unit 410 as described herein, e.g., encrypt and encodedata during a write operation, manage the dispersal of slices of dataduring a write operation, and decode and decrypt data during a readoperation, etc. The DS processing unit program module 415 may compriseone or more program modules 42 as described with respect to FIG. 1.

According to aspects of the invention, the DS units 420 are configuredto store the data slices that are received from a DS processing unit 410during a write, and to return data slices to a DS processing unit 410during a read. Each DS unit 420 may comprise a physical device (e.g., acomputer device such as a computer system/server 12 of FIG. 1), avirtual machine (VM), or a container (e.g., a Docker container).

In embodiments, each DS unit 420 comprises DS unit program module 425and data storage 427. The DS unit program module 425 may comprise one ormore program modules 42 as described with respect to FIG. 1, and isconfigured to perform processes of the DS unit 420 as described herein,e.g., store data slices that are received from a DS processing unit 410during a write, return data slices to a DS processing unit 410 during aread, perform compaction of data in the data storage 427, determine aparticular zone in the data storage 427 in which to store each dataslice, etc.

In embodiments, the data storage 427 receives and stores data inaccordance with instructions received from the DS unit program module425. The data storage 427 is one or more of any type or combination oftypes of data storage medium, data storage device, or system (e.g.,storage device 65 of FIG. 3) and is located on (or is accessible to) theDS unit 420. For example, the data storage 427 may include one or morehard drives, SMR (Shingled Magnetic Recording) drives, solid statedrives (SSDs), Tape Drives, and other memory devices.

In implementations, a client device 450 runs a client application thatcommunicates with one of the DS processing units 410 to perform dataoperations in the DSN 400. In embodiments, the client application usesapplication programming interfaces (APIs) to perform data operations inthe DSN 400. In one example, a first API call (e.g., PUT) writes a dataobject to the DSN 400, a second API call (e.g., GET) reads a data objectfrom the DSN 400, a third API call (e.g., DELETE) deletes a data objectfrom the DSN 400, and a fourth API call (e.g., LIST) lists all the dataobjects in a bucket in the DSN 400. In embodiments, the client device450 comprises a computer device such as a laptop computer, desktopcomputer, tablet computer, etc., and may comprise one or more componentsof the computer system/server 12 of FIG. 1. In embodiments, the clientapplication running on the client device 450 is a software application,and may comprise one or more program modules 42 as described withrespect to FIG. 1. In embodiments, the client device 450 communicateswith one of the DS processing units 410 via the network 440.

FIGS. 5-10 illustrate an exemplary operation of the DSN 400. FIG. 5 is aschematic block diagram of an example of dispersed storage errorencoding of data. When a DS processing unit 410 has data to store itdisperse storage error encodes the data in accordance with a dispersedstorage error encoding process based on dispersed storage error encodingparameters. Here, the computing device stores a data object, which caninclude a file (e.g., text, video, audio, etc.), or other dataarrangement. The dispersed storage error encoding parameters include anencoding function (e.g., information dispersal algorithm (IDA),Reed-Solomon, Cauchy Reed-Solomon, systematic encoding, non-systematicencoding, on-line codes, etc.), a data segmenting protocol (e.g., datasegment size, fixed, variable, etc.), and per data segment encodingvalues. The per data segment encoding values include a total, or pillarwidth, number (T) of encoded data slices per encoding of a data segmenti.e., in a set of encoded data slices); a decode threshold number (D) ofencoded data slices of a set of encoded data slices that are needed torecover the data segment; a read threshold number (R) of encoded dataslices to indicate a number of encoded data slices per set to be readfrom storage for decoding of the data segment; and/or a write thresholdnumber (W) to indicate a number of encoded data slices per set that mustbe accurately stored before the encoded data segment is deemed to havebeen properly stored. The dispersed storage error encoding parametersmay further include slicing information (e.g., the number of encodeddata slices that will be created for each data segment) and/or slicesecurity information (e.g., per encoded data slice encryption,compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 6 and a specificexample is shown in FIG. 7); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the DS processing unit 410 divides data objectinto a plurality of fixed sized data segments (e.g., 1 through Y of afixed size in range of Kilo-bytes to Tera-bytes or more). The number ofdata segments created is dependent of the size of the data and the datasegmenting protocol.

The DS processing unit 410 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 6 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 7 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 5, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 480 is shown inFIG. 8. As shown, the slice name (SN) 480 includes a pillar number ofthe encoded data slice (e.g., one of 1-T), a data segment number (e.g.,one of 1-Y), a vault identifier (ID), a data object identifier (ID), andmay further include revision level information of the encoded dataslices. The slice name functions as, at least part of, a DSN address forthe encoded data slice for storage and retrieval from the DSN memory.

As a result of encoding, the DS processing unit 410 produces a pluralityof sets of encoded data slices, which are provided with their respectiveslice names to the storage units (DS unit 420 numbers one through fivein this example) for storage. As shown, the first set of encoded dataslices includes EDS 1_1 through EDS 5_1 and the first set of slice namesincludes SN 1_1 through SN 5_1 and the last set of encoded data slicesincludes EDS 1_Y through EDS 5_Y and the last set of slice namesincludes SN 1_Y through SN 5_Y.

FIG. 9 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 6. In this example, the DS processingunit 410 retrieves from the storage units at least the decode thresholdnumber of encoded data slices per data segment. As a specific example,the computing device retrieves a read threshold number of encoded dataslices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.10. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 6. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

In an example of operation and implementation, memory devices (orparadigms of storage within a DSN such as DS units therein) may operatemost efficiently in an append-only or append-heavy workload. Forexample, hard drives, SMR (Shingled Magnetic Recording) drives, solidstate drives (SSDs), Tape Drives, and other memory devices may beimplemented within a DSN to store data in the form of slices asdescribed herein. Similarly, some mechanisms for storing slices, PackedSlice Storage (PSS), and Zone Slice Storage (ZSS) are designed such thatmost or all new writes are done in a way that they are appended to asequential log. Such forms of storage naturally lead to a situationwhere overwrites or deletes of stored slices create holes in the log.The log will continue to hold content that is no longer active data(such as old revisions of slices that have been finalized or undone, oroverwritten by delete markers). To reclaim the space associated with theholes while continuing to operate in an append-only mode can requirere-writing the log but skipping any entries associated with deleted orobsolesced slices.

In accordance with aspects of the invention, each DS unit 420 uses ZoneSlice Storage (ZSS) to store data in its data storage 427. ZSS is astorage paradigm that is implemented by the DS unit program module 425at each DS unit 420. In ZSS, the DS unit program module 425 defineszones of fixed size (e.g., typically 256 MB) in the storage media (e.g.,data storage 427). In embodiments, the DS unit program module 425sequentially stores slices received from DS processing units 410 inzones, with many slices from many different data objects being storedtogether in a respective zone. In embodiments, the DS unit programmodule 425 utilizes a fully sequential write protocol. For example, astorage unit (e.g., DS unit 420) can be implemented by utilizing AppendOptimal Storage Devices (AOSD) or other memory devices for whichappended writes are the optimal form of access, and/or for which anappend-only write scheme is utilized when storing data. The append-onlywrite scheme dictates that new data slices are written by being appendedto an end, or “append point” of a zone in storage, such as storage zone.As data slices are written, they are written to the next space in theirrespective zone of memory according to the corresponding append point ofthe zone, and the append point is updated based on the length of newlywritten data. Append points for each zone can be maintained in avolatile memory such as RAM or other memory of the storage unit, and canbe stored as a pointer or other reference to the append point locationof the memory device.

The storage unit (e.g., DS unit 420) can dynamically allocate new zonesand un-allocate old zones of one or more memory devices to maintain afixed number of active zones and/or a number of active zones that isdetermined to be optimal. The number of zones and/or the zones selectedin the subset can be determined based on zone allocation parametersand/or zone reallocation parameters, which can be based on I/O requestfrequency, memory and/or processing requirements, I/O speedrequirements, and/or other zone allocation and/or reallocationrequirements. Selecting a smaller subset of zones open for write canfurther minimize seeking and thus improve I/O speed. In someembodiments, exactly one zone per memory device is open for writing atany given time. This can eliminate seeking on each memory device aswriting is fully sequential on each memory device. In variousembodiments, the active zone can be selected based on available space inthe zone, based on a previously selected zone, and/or selected randomly.The storage unit can maintain information regarding which zones aredesignated as open to writes and/or reads, and which zones are closed towrites and/or reads, and can change these designations in response todetermining a reallocation requirement is met. The storage unit can alsomaintain zone priority information and/or available capacity informationfor each of the zones. This information can be stored in RAM or othermemory of the storage unit.

Fully sequential writing and the log-based data structure employed inZSS leads to unreferenced space in zones when, for example, a slice thatis stored in a zone is subsequently revised or deleted. Suchunreferenced space is referred to as holes, and ZSS uses compaction toreclaim the space occupied by holes. To reclaim space in a zone that hasboth holes and live data (i.e., slices that are not revised or deleted),the storage unit (e.g., DS unit 420) reads the entire zone andsequentially writes the live data in a new zone, with no holes. The livedata is compacted in the new zone, and the initial zone is released forfuture allocation (e.g., for sequentially writing new incoming slices inthe entirety of this zone). In this manner, the storage unit reclaimsthe unreferenced space that was occupied by the holes in the initialzone. The amount of space reclaimed depends on how much unreferencedspace is in a zone. The edge compaction case is when no live data is ina zone, in which case compaction turns into I/O NOOP. This is a singularcase in which compaction is virtually free. Thus it is seen thatcompaction efficiency is a factor that affects overall ZSS efficiency.

In accordance with aspects of the invention, each DS unit 420 uses ZSSand defines three types of zones: a first type zone for storing data(e.g., slices) that is likely to be deleted very soon (such that thistype of zone is likely to be reclaimed without performing actual I/Ooperations); a second type zone for storing data (e.g., slices) writtenduring a normal operation (e.g., data that does not satisfy the firsttype zone and that is not written during compaction); and a third typezone for storing data (e.g., slices) that is written during compaction.

Embodiments are configured on an expectation that the third type zone isthe least likely to have dead slices and thus the least likely to be thesubject of compaction. For example, data that is not overwritten (e.g.,revised) or deleted for significant amount of time has a high likelihoodto be kept in the future. Embodiments are also configured on anexpectation that the first type zone is expected to be efficientlycompacted. For example, a zone that is fully populated with data thatthe system determines will be overwritten (e.g., revised) or deleted ina relatively short amount of time can be reclaimed with no I/Ooperations, since there is no live data to write to a new zone. Thefirst type zone and the third type zone thus define opposite ends of aspectrum, with the second type zone occupying a space there between. Forexample, in embodiments, the second type zone is used to store data thatis a mixture of long-lived and short-lived objects, as no assumption ismade about object modification pattern in the future.

As described herein, some data objects are the subject of frequentmodifications due to application logic. In embodiments, there aremultiple ways to discern a frequently update access pattern (e.g.,predict a modification frequency) of such a data object, including butnot limited to: via application awareness (for example, based on storagetype or observed modification sequence, by object type); via client'shint; via learning process (including machine learning); and viahistoric per object modification trends.

An example of application awareness based on storage type is as follows.The DSN stores data received from external clients such as client device450 in FIG. 4. However, the DSN also stores data that is generatedinternally by applications running processes within the DSN. One suchexample is a temporary copy of a data object that the DSN generatesinternally. Another such example is a container index, which is aspecialized namespace used to store data defining a configuration stateof all buckets in a DSN memory. Another such example is a leasable index(e.g., Dispersed Lockless Concurrent Index (DLCI)), which is a datastructure that contains a queue of work items, where plural DSprocessing units 410 act in parallel to lease individual ones of thework items and process the leased work items. Each of these examples ofinternal data may be stored as slices in the DSN, similar to and withslices received from client devices 450. In embodiments, the system isaware that certain types of internal data are updated with relativelyhigh frequency. For example, a temporary copy of a data object might bedeleted within less than a minute of being stored. In another example, acontainer index might be updated daily, and a leasable index might beupdated every few minutes. Accordingly, in embodiments, the systemleverages this knowledge of its own internal data to define certaininternal storage types (system storage types) that are likely to bemodified relatively soon after they are stored, and stores slices forthese storage types in the first type zone (e.g., also referred to as aTransient zone). In embodiments, the system uses this data to predict amodification frequency for an object of a particular storage types,e.g., based on observed historic modification frequencies for otherobjects of the same storage type.

An example of application awareness based on observed modificationsequence is as follows. In embodiments, the system determinescharacteristics of data objects and monitors client level modificationsperformed on data objects. In this manner, the system is programmed tolearn patterns of client modifications of data objects having certaintypes of characteristics. Characteristics can include one or more of:owner; file type; file name; date saved to the DSN (e.g., particular dayof the week or day of the month); time of day saved to the DSN; filesize; and object lifecycle management rule(s) associated with the dataobject. Based on patterns determined in this manner, the system maydetermine that a data object having a certain set of characteristics isalways modified (e.g., deleted or overwritten) in a short time periodafter it is first saved. As a result, the system may determine that dataobjects having this certain set of characteristics are stored in thefirst type zone (e.g., a Transient zone). In embodiments, the systemuses this data to predict a modification frequency for an object e.g.,based on observed historic modification frequencies for other objects ofthe same observed modification sequence.

An example of client hint is as follows. In embodiments, the system mayprovide the user with a mechanism (e.g., via client device 450) toindicate that a data object being saved to the DSN is a short termobject that will be modified relatively soon. As a result, the systemmay determine that data objects indicated in this manner by a client arestored in the first type zone (e.g., a Transient zone).

An example of learning process via machine learning is as follows. Inembodiments, the system determines characteristics of internal dataobjects and monitors system level modifications performed on theinternal data objects. In this manner, the system is programmed to learnpatterns of system modifications of internal data objects having certaintypes of characteristics. Characteristics can include one or more of:application that created the data object in the DSN; file type; filename; date saved to the DSN (e.g., particular day of the week or day ofthe month); time of day saved to the DSN; file size; and objectlifecycle management rule(s) associated with the data object. Based onpatterns determined in this manner, the system may determine that aninternal data object having a certain set of characteristics is alwaysmodified (e.g., deleted or overwritten) in a short time period after itis first saved. As a result, the system may determine that internal dataobjects having this certain set of characteristics are stored in thefirst type zone (e.g., a Transient zone). In embodiments, the systemuses this data to predict a modification frequency for an object e.g.,based on observed historic modification frequencies for other internaldata objects having certain types of characteristics.

In embodiments, and based on these concepts, an exemplary operation of aDS unit program module 425 is as follows. A slice arrives at the DS unit420 from the DS processing unit 410, and the DS unit program module 425determines whether this slice belongs in the first type zone (e.g., aTransient zone). In embodiments, the DS unit program module 425 makesthis determination by predicting a modification frequency of the dataobject associated with the slice based on at least one of: applicationawareness (for example, based on storage type or observed modificationsequence, by object type); client's hint; learning process (includingmachine learning); and historic per object modification trends. In theevent that this slice is determined to belong in the first type zone(e.g., predicted to have modification frequency higher than a predefinedthreshold value), then the DS unit program module 425 saves this slicein a zone of the data storage 427 that is defined as the first typezone. In embodiments, the DS unit program module 425 sequentially writesthis slice to the determined first type zone by appending it to an end(or append point) of the zone. In this manner, first type zones of thedata storage 427 are populated with slices from various data objectsthat are determined by the system to have a relatively high modificationfrequency and, thus, a relatively short time before they are overwrittenor deleted.

In the event that this slice is determined to not belong in the firsttype zone (e.g., predicted to have modification frequency lower than thepredefined threshold value), then the DS unit program module 425 savesthis slice in a zone of the data storage 427 that is defined as thesecond type zone (also referred to as an Eden zone). In embodiments, theDS unit program module 425 sequentially writes this slice to thedetermined second type zone by appending it to an end (or append point)of the zone.

Still referring to the same exemplary operation, at some later time, theDS unit program module 425 determines that a second type zone reachesthe threshold for compaction. All live slices in this identified secondtype zone are moved to a third type zone (also called a Tenured zone),and this reclaimed second type zone is then available for re-allocation.Any particular third type zone may have slices from plural differentsecond type zones that survived different compaction processes. In thismanner, third type zones of the data storage 427 are populated withslices from various data objects that are determined by the system tohave a relatively low modification frequency (e.g., because theysurvived the compaction) and, thus, a relatively long time before theyare overwritten or deleted.

Still referring to the same exemplary operation, at some later time, theDS unit program module 425 reclaims a first type zone without performingany I/O, i.e., without writing any slices to another zone, since all theslices are presumed to be overwritten or deleted.

In a particular exemplary embodiment, the data storage 427 is a 10terabyte SMR drive, and the DS unit program module 425 uses ZSS todefine 10,000 zones each of 256 megabytes in the SMR drive, where eachof the defined zones is mapped directly to a physical zone of the SMRdrive. In this exemplary embodiment, the DS unit program module 425designates the defined zones as one of a first type zone, a second typezone, and a third type zone as described herein.

FIG. 11 depicts a flowchart of an exemplary method in accordance withaspects of the invention. In embodiments, the steps of the method areperformed in the environment of FIGS. 4-10 and are described withreference to the elements shown in FIGS. 4-10.

At step 1101, the DS unit 420 receives a slice for storage. Inembodiments, and as described with respect to FIGS. 4-10, the DS unit420 receives a slice from a DS processing unit 410. The slice may beassociated with a data object received from a client device 450, or maybe associated with an internal data object created within the DSN.

At step 1102, the DS unit program module 425 predicts a modificationfrequency of the slice that was received at step 1101. In embodiments,and as described with respect to FIGS. 4-10, the DS unit program module425 predicts a modification frequency of the slice based on at least oneof: application awareness (for example, based on storage type orobserved modification sequence, by object type); client's hint; learningprocess (including machine learning); and historic per objectmodification trends.

At step 1103, the DS unit program module 425 determines whether theslice should be saved in a first type zone. In embodiments, and asdescribed with respect to FIGS. 4-10, the DS unit program module 425determines the slice should be saved in a first type zone when thepredicted modification frequency of the slice is greater than apredefined threshold value (i.e., higher frequency than a thresholdfrequency), and the DS unit program module 425 determines the sliceshould not be saved in a first type zone when the predicted modificationfrequency of the slice is less than the predefined threshold value(i.e., lower frequency than the threshold frequency).

In the event the DS unit program module 425 determines the slice shouldbe saved in a first type zone, then at step 1104 the DS unit programmodule 425 saves the slice in a first type zone of the data storage 427.In embodiments, the DS unit program module 425 sequentially writes thisslice to a first type zone defined in the data storage 427 by appendingit to an end (or append point) of the first type zone.

In the event the DS unit program module 425 determines the slice shouldnot be saved in a first type zone, then at step 1105 the DS unit programmodule 425 saves the slice in a second type zone of the data storage427. In embodiments, the DS unit program module 425 sequentially writesthis slice to a second type zone defined in the data storage 427 byappending it to an end (or append point) of the second type zone.

After either step 1104 or step 1105, one path of the process returns tostep 1101 to wait for another slice to arrive at the DS unit 420 forstorage. In this manner, plural slices from plural different dataobjects may be written into a respective first type zone, and pluralslices from plural different data objects may be written into arespective first type zone, and there may be plural first type zones andplural second type zones in the data storage 427.

At some time after either step 1104 or step 1105, the DS unit programmodule 425 reclaims the first type zone at step 1106. In embodiments,and as described with respect to FIGS. 4-10, the DS unit program module425 reclaims the first type zone without performing any I/O operations,i.e., without writing any slices from the reclaimed zone to a new zone.For example, the DS unit program module 425 may reset the append pointto the beginning of the zone and re-allocate the zone. In this mannerthe first type zone is reclaimed in the most efficient manner possible(i.e., without performing any I/O operations). The time to reclaim afirst type zone may be defined by the system, and may comprise, forexample, a predefined amount of time after the last slice was written tothe zone.

At some time after either step 1104 or step 1105, the DS unit programmodule 425 reclaims the second type zone at step 1107. In embodiments,and as described with respect to FIGS. 4-10, the DS unit program module425 determines the second type zone reaches a threshold for compaction.The threshold may be, for example, a threshold amount space in the zoneoccupied by holes. Upon making this determination, the DS unit programmodule 425 reads the entire zone, and at step 1108 moves all the liveslices from the second type zone being reclaimed to a third type zone.The third type zone may be a newly allocated third type zone, or may bean previously allocated third type zone that already contains slicesfrom one or more other second type zones that were moved during acompaction process.

In embodiments, a service provider could offer to perform the processesdescribed herein. In this case, the service provider can create,maintain, deploy, support, etc., the computer infrastructure thatperforms the process steps of the invention for one or more customers.These customers may be, for example, any business that uses technology.In return, the service provider can receive payment from the customer(s)under a subscription and/or fee agreement and/or the service providercan receive payment from the sale of advertising content to one or morethird parties.

In still additional embodiments, the invention provides acomputer-implemented method, via a network. In this case, a computerinfrastructure, such as computer system/server 12 (FIG. 1), can beprovided and one or more systems for performing the processes of theinvention can be obtained (e.g., created, purchased, used, modified,etc.) and deployed to the computer infrastructure. To this extent, thedeployment of a system can comprise one or more of: (1) installingprogram code on a computing device, such as computer system/server 12(as shown in FIG. 1), from a computer-readable medium; (2) adding one ormore computing devices to the computer infrastructure; and (3)incorporating and/or modifying one or more existing systems of thecomputer infrastructure to enable the computer infrastructure to performthe processes of the invention.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method, comprising: receiving, by a computingdevice, a data slice for storage in a dispersed storage network;predicting, by the computing device, a modification frequency associatedwith the data slice; and storing, by the computing device, the dataslice in one of a first type zone of a data storage and a second typezone of the data storage based on the predicted modification frequency.2. The method of claim 1, further comprising storing plural differentdata slices in a sequential manner in a single first type zone of thedata storage, based on each of the plural different data slices having apredicted modification frequency exceeding a threshold value.
 3. Themethod of claim 2, further comprising reclaiming the single first typezone after a predefined time period without performing any I/Ooperations.
 4. The method of claim 1, further comprising storing pluraldifferent data slices in a sequential manner in a single second typezone of the data storage, based on each of the plural different dataslices having a predicted modification frequency less than a thresholdvalue.
 5. The method of claim 4, further comprising reclaiming thesingle second type zone based on determining the single second type zonereaches a compaction threshold.
 6. The method of claim 5, wherein thereclaiming the single second type zone comprises: sequentially writingall live data slices in the single second type zone to a third typezone; and reallocating the single second type zone.
 7. The method ofclaim 6, further comprising reclaiming at least one additional secondtype zone by sequentially writing all live data slices in the at leastone additional second type zone to the third type zone.
 8. The method ofclaim 1, wherein the computing device predicts the modificationfrequency based on one of: application awareness; client hint; machinelearning process; and historic per object modification trends
 9. Themethod of claim 1, wherein: the computing device is a dispersed storageunit in the dispersed storage network; and the data storage comprises atleast one drive.
 10. The method of claim 9, wherein: the dispersedstorage unit uses Zone Slice Storage; each of the first type zone andthe second type zone maps to a respective physical zone of the at leastone drive.
 11. A computer program product, the computer program productcomprising one or more computer readable storage media having programinstructions collectively stored on the one or more computer readablestorage media, the program instructions executable to: define differentzones of a data storage of a dispersed storage unit in a dispersedstorage network, the different zones including first type zones, secondtype zones, and third type zones; receive plural data slices for storagein the dispersed storage unit; store a first subset of the plural dataslices in a said first type zone based on predicting modificationfrequencies associated with slices in the first subset; store a secondsubset of the plural data slices, that is mutually exclusive of thefirst subset of the plural data slices, in a said second type zone;reclaim the said first type zone without performing any I/O operations;and reclaim the said second type zone by writing live slices containedin the said second type zone to a third type zone and then reallocatingthe said second type zone.
 12. The computer program product of claim 11,wherein the predicting modification frequencies is based on one of:application awareness; client hint; machine learning process; andhistoric per object modification trends.
 13. The computer programproduct of claim 11, wherein the reclaiming the said first type zone isperformed at a predefined time period after a last slice of the firstsubset is written to the said first type zone.
 14. The computer programproduct of claim 11, wherein the reclaiming the said second type zone isperformed in response to determining the single second type zone reachesa compaction threshold.
 15. The computer program product of claim 11,wherein: the dispersed storage unit uses Zone Slice Storage; the datastorage comprises at least one drive; and each instance of the firsttype zone, the second type zone, and the third type zone maps to arespective physical zone of the at least one drive.
 16. A systemcomprising: a dispersed storage unit in a dispersed storage network, thedispersed storage unit comprising a processor, a computer readablememory, one or more computer readable storage media, and programinstructions collectively stored on the one or more computer readablestorage media for execution by the processor, wherein execution of theprogram instructions causes the dispersed storage unit to: definedifferent zones of a data storage of the dispersed storage unit, thedifferent zones including first type zones, second type zones, and thirdtype zones; receive plural data slices for storage in the dispersedstorage unit; store a first subset of the plural data slices in a saidfirst type zone based on predicting modification frequencies associatedwith slices in the first subset; store a second subset of the pluraldata slices, that is mutually exclusive of the first subset of theplural data slices in a said second type zone; reclaim the said firsttype zone without performing any I/O operations; and reclaim the saidsecond type zone by writing live slices contained in the said secondtype zone to a third type zone and then reallocating the said secondtype zone.
 17. The system of claim 16, wherein the predictingmodification frequencies is based on one of: application awareness;client hint; machine learning process; and historic per objectmodification trends.
 18. The system of claim 16, wherein the reclaimingthe said first type zone is performed at a predefined time period aftera last slice of the first subset is written to the said first type zone.19. The system of claim 16, wherein the reclaiming the said second typezone is performed in response to determining the single second type zonereaches a compaction threshold.
 20. The system of claim 16, wherein: thedispersed storage unit uses Zone Slice Storage; the data storagecomprises at least one drive; and each instance of the first type zone,the second type zone, and the third type zone maps to a respectivephysical zone of the at least one drive.